Stock price manipulation:Prevalence and determinants

We empirically analyze the prevalence and economic underpinnings of closing price manipulation and its detection. We estimate that ∼1% of closing prices are manipulated, of which only a small fraction is detected and prosecuted. We find that stocks with high levels of information asymmetry and mid to low levels of liquidity are most likely to be manipulated. A significant proportion of manipulation occurs on month/quarter-end days. Manipulation on these days is more likely in stocks with high levels of institutional ownership. Government regulatory budget has a strong effect on both manipulation and detection

Shorting at Close Range: A Tale of Two Types

Abstract We examine stock returns, order flow, and market conditions in the minutes before, during, and after recent short sales on the NYSE and Nasdaq. We find two very distinct types of short sales: those that provide liquidity, and those that demand it. Shorts that supply liquidity do so when spreads are unusually wide. These short sellers are also strongly contrarian, stepping in to initiate or increase a short position after fairly sharp share price rises over the past hour or so, and they tend to face greater adverse selection than other liquidity suppliers. In contrast, shorts that demand liquidity tend to be shortterm momentum traders. However, there is no evidence that liquidity-demanding short sellers are any different from other liquidity demanders. Overall, liquidity-providing short sales are important contributors to stock market quality, and regulators and policymakers should keep these salutary effects in mind. JEL classification: G14, G1

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Is Australia HFT-friendly?

Stephen Satchell's paper 'An assessment of the social desirability of high-frequency trading', in this issue of JASSA examines the costs and benefits, and highlights some empirical evidence on the impact of HFT on market quality and welfare. Building on Satchell's paper, this paper provides a perspective on HFT in the Australian market and identifies the factors influencing its attractiveness to HFT players. It also compares the US and Australian markets in terms of these factors to indicate the growth prospects for HFT activity in Australia